Artificial Intelligence In Financial Services: Advancements In Fraud Detection, Risk Management, And Algorithmic Trading Optimization

Published: Jan. 1, 2025

Fraud detection, risk management, and algorithmic trading optimization are being revolutionized by AI in financial services. reduces false positives speeds up fraud detection spotting trends anomalies real time using advanced machine learning techniques. Financial institutions can now fight sophisticated cyber attacks with AI-powered systems that analyze massive databases detect illicit conduct unparalleled accuracy. predictive analytics changing how organizations identify mitigate risks. Institutions predict credit defaults, market swings, operational weaknesses big data AI. Natural language processing (NLP) techniques extracting insights from unstructured sources including regulatory filings news to improve decision-making. Real-time monitoring enable proactive interventions reduce losses assure compliance. is transforming trading, another breakthrough. Advanced models historical live price movements, find arbitrage opportunities, execute trades milliseconds. Reinforcement helping design adaptable algorithms respond changes, increasing profitability reducing risk. also promotes ethical transparent tactics, solving manipulation problems. This study analyses the newest applications services their disruptive influence. Generative AI, federated learning, quantum computing will further transform sector. adoption has many benefits, but privacy, bias, legal complexity must be addressed sustain progress. efficiency, resilience, creativity, creating a future where technology drives trust strategic advantage.

Language: Английский

---

Artificial Intelligence In Retail And E-Commerce: Enhancing Customer Experience Through Personalization, Predictive Analytics, And Real-Time Engagement

Published: Jan. 1, 2025

AI is transforming retail and e-commerce with unprecedented personalization, predictive analytics, real-time customer involvement. AI-powered recommendation engines, chatbots, sentiment analysis tools enable customer-centric tactics as consumers want more personalized experiences. AI's capacity to analyze massive volumes of data allows merchants develop shopping experiences that boost pleasure loyalty. For instance, deep learning-based systems accurately predict client preferences, increasing conversion rates average order values. analytics changing inventory management, demand forecasting, pricing in retail. Stock levels, waste, profitability are optimized by machine learning algorithms examine historical sales data, market trends, behavior. Real-time insights dynamic models adjust instantaneously supply changes, maintaining competitiveness fast-paced e-commerce. AI-enabled engagement business-customer interactions. Conversational can answer questions instantly personally smart chatbots voice assistants, improving user experience lowering operational expenses. Visual technologies like image identification augmented reality virtual try-ons visual search, online purchasing. The use has highlighted ethical issues such privacy algorithmic fairness. Growing sustainably requires balancing consumer personalization trust. This paper examines how might improve experience, supported recent breakthroughs industry trends. It shows may purchasing while tackling implementation a digital economy.

Language: Английский

---

Additive Manufacturing Modification by Artificial Intelligence, Machine Learning, and Deep Learning: A Review

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 200198 - 200198

Published: Feb. 1, 2025

Language: Английский

---

Artificial Intelligence-Driven Customer Service: Enhancing Personalization, Loyalty, And Customer Satisfaction

Published: Jan. 1, 2025

AI-driven customer service is revolutionizing how businesses interact with customers by improving personalization, loyalty, and satisfaction through data-driven insights responsive interactions. AI technologies like machine learning (ML), natural language processing (NLP), generative models allow companies to scale experiences that match individual preferences, behaviors, needs. tools in service, such as chatbots virtual assistants, are response times issue resolution, increasing loyalty. Companies can analyze massive datasets real time using improve profiles predict future systems boost brand loyalty personalizing interactions making feel valued. Additionally, ChatGPT engagement reducing friction providing human-like responses conversational experiences. sentiment analysis help anticipate dissatisfaction assessing emotions feedback. Along AI-based solutions programs them more dynamic engaging. Businesses identify high-value customers, personalize offers, encourage repeat business predictive analytics. Despite these advances, ethical issues data privacy interaction must be addressed. As evolves, balancing automation personalized human crucial. This paper examines current trends, case studies, developments demonstrate transform environments into customer-centric, responsive, adaptable ones foster long-term satisfaction.

Language: Английский

---

Internet of Robotic Things: Current Technologies, Challenges, Applications, and Future Research Topics

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 765 - 765

Published: Jan. 27, 2025

This article focuses on the integration of Internet Things (IoT) and Robotic Things, representing a dynamic research area with significant potential for industrial applications. The (IoRT) integrates IoT technologies into robotic systems, enhancing their efficiency autonomy. provides an overview used in IoRT, including hardware components, communication technologies, cloud services. It also explores IoRT applications industries such as healthcare, agriculture, more. discusses challenges future directions, data security, energy efficiency, ethical issues. goal is to raise awareness importance demonstrate how this technology can bring benefits across various sectors.

Language: Английский

---